## data_algebra 0.7.0 What is New

I’ve been tinkering a lot recently with the data_algebra, and just released version 0.7.0 to PyPi. In this note I’ll touch on what the data algebra is, what the new features are, and my plans going forward.

I’ve been tinkering a lot recently with the data_algebra, and just released version 0.7.0 to PyPi. In this note I’ll touch on what the data algebra is, what the new features are, and my plans going forward.

I have up what I think is a really neat tutorial on how to plot multiple curves on a graph in Python, using seaborn and data_algebra. It is great way to show some data shaping theory convenience functions we have developed. Please check it out.

There’s a common, yet easy to fix, mistake that I often see in machine learning and data science projects and teaching: using classification rules for classification problems. This statement is a bit of word-play which I will need to unroll a bit. However, the concrete advice is that you often […]

We have a new improved version of the “how to design a cdata/data_algebra data transform” up! The original article, the Python example, and the R example have all been updated to use the new video. Please check it out!

Nina Zumel and I have a two new tutorials on fluid data wrangling/shaping. They are written in a parallel structure, with the R version of the tutorial being almost identical to the Python version of the tutorial. This reflects our opinion on the “which is better for data science R […]

We’ve been experimenting with this for a while, and the next R vtreat package will have a back-port of the Python vtreat package sklearn pipe step interface (in addition to the standard R interface).

For quite a while we have been teaching estimating variable re-encodings on the exact same data they are later naively using to train a model on, leads to an undesirable nested model bias. The vtreat package (both the R version and Python version) both incorporate a cross-frame method that allows […]

I’d like to share some new timings on a grouped in-place aggregation task. A client of mine was seeing some slow performance, so I decided to time a very simple abstraction of one of the steps of their workflow.

I’ve been writing a lot about a category theory interpretations of data-processing pipelines and some of the improvements we feel it is driving in both the data_algebra and in rquery/rqdatatable. I think I’ve found an even better category theory re-formulation of the package, which I will describe here.

In our recent note What is new for rquery December 2019 we mentioned an ugly processing pipeline that translates into SQL of varying size/quality depending on the query generator we use. In this note we try a near-relative of that query in the data_algebra.